Recently many efforts have been undertaken to make process management systems (PMS) more flexible and several approaches for adaptive process management have emerged. The basic idea behind them is to enable dynamic changes of different process aspects (e.g., control / data flow, resources) and at different process levels. For example, ad-hoc changes conducted at the process instance level (e.g., to add or shift steps) make it possible to flexibly adapt single process instances to exceptional or changing situations. As a positive side-effect we obtain much more meaningful process logs when compared to existing PMS. So far, adaptive PMS have not addressed the fundamental question what we can learn from this additional information and how to derive optimized process models from it. Process mining techniques offer promising perspectives, but have focused on the analysis of pure execution logs so far. The MinADEPT project will close this gap and provide a comprehensive approach for the intelligent mining of adaptive processes.

This involves three problems: First, we have to determine which information about ad-hoc deviations should be logged to achieve optimal mining results. Second, we have to develop advanced mining techniques which utilize execution and change logs. Third, we have to integrate these techniques with existing adaptive PMS in order to provide full process life cycle support. With ADEPT and ProM the proposal aims at the integration and extension of two of the most powerful frameworks existing in this context.